Three Steps to Manage Customer Data

Infuse Analytics and Recommendations

Marketing, e-commerce, and support teams will gain insight into consumer desires, habits, product interests, and channel selection with data combined into one location. Based on full knowledge, companies can understand the needs of clients and have customized experiences. Data-driven customer applications give insights such as churn propensity, lifetime value, and abandonment rates. Machine learning and predictive analytics can be used to recommend the next best steps to provide clients appropriate and reliable data and identify opportunities for upselling and cross-selling.

See Also: Top Martech Companies

Build Reliable Customer Profiles

A robust customer data management system must bring all customer information (people and organizations) to one location and make it available in real-time. Data from internal, external, and third-party sources is combined to create accurate and reliable profiles of individuals and accounts. In addition to consolidating customer master records, to provide companies with the full picture, the data platform must also construct detailed profiles with correlated transactions and interactions. It is also important to regularly clean and arrange all customer information for ongoing insight and interaction.

Correlate Master, Interaction, and Transactional Data

As clients analyze, evaluate, or purchase goods in today's multichannel interaction environment, marketers want to understand consumer needs. Omnichannel support for marketing, sales, and service operations often include a clear and full understanding of consumer habits and expectations. This is where a lot of MDM devices fall short. Blending omnichannel data with master profiles allows companies to recognize consumer product preferences and channel preferences and can provide advanced insights with this knowledge to assess the next-best possible interaction behavior.